Risk Behavior-Based Trajectory Prediction for Construction Site Safety Monitoring
Publication: Journal of Construction Engineering and Management
Volume 144, Issue 2
Abstract
Construction sites are often described as some of the most hazardous work environments due to the unscripted nature of tasks which puts workers and equipment in close proximity, potentially resulting in near-miss situations or life-threatening contact collisions. Previous research has investigated location-aware methods to improve construction safety but has mostly fallen short in exploring the extent to which prediction techniques can be used to model and formulate the role and attributes of individual workers in addition to the physical characteristics of the jobsite that may lead to safety incidents. This paper studies the feasibility of a preemptive proximity-based safety framework by investigating two trajectory prediction models, namely polynomial regression (PR) and hidden Markov model (HMM). The HMM prediction is further calibrated by factoring in a worker’s risk profile, which is a measure of his or her affinity for or aversion to risky behavior near hazards. The method is tested in a series of field experiments involving trajectories of different shapes and complexity. Results demonstrate that the developed methodology can reliably detect unsafe movements and impending collision events.
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Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author by request. Information about the Journal’s data sharing policy can be found here: http://ascelibrary.org/doi/10.1061/%28ASCE%29CO.1943-7862.0001263.
Acknowledgments
The authors thank Mr. Nipun D. Nath for his support in conducting some of the field experiments.
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Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 144 • Issue 2 • February 2018
Copyright
©2017 American Society of Civil Engineers.
History
Received: Feb 28, 2017
Accepted: Jul 19, 2017
Published online: Nov 27, 2017
Published in print: Feb 1, 2018
Discussion open until: Apr 27, 2018
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